INFRA | SOFTWARE | HARDWARE
Training a neural network sounds mystical, but the engine underneath is one idea from first-year calculus: the chain rule, applied backwards through a computation graph and reusing its work. We trace a forward and backward pass through a tiny graph, see why we run it in reverse, and connect it to the downhill step that actually does the learning.

Image generators start from pure TV static and end with a photo. The trick that makes it possible is wonderfully sneaky: don't learn to paint, learn to remove a little noise, then run that backwards from static. We build the forward noising process step by step, see the signal-versus-noise schedule, and work out why predicting noise is such a clever thing to train.

Conway's Game of Life has three rules and a grid of on-or-off cells, and nothing else. From that, gliders crawl, guns fire, and — astonishingly — you can build a working computer. We play with the rules, watch structure emerge that nobody designed, and follow the thread to its unsettling end: a system this simple can be impossible to predict.

Small, working simulations — drag the controls on the full pages, or just watch the previews cycle here. The selection rotates daily.